Created Augmented pred/ipred plots with `augPred()`

This is a simple process to create individual predictions augmented with more observations than was modeled. This allows smoother plots and a better examination of the observed concentrations for an individual and population.

Step 1: Convert the NONMEM model to rxode2:

library(babelmixr2)
library(nonmem2rx)

# First we need the location of the nonmem control stream Since we are running an example, we will use one of the built-in examples in `nonmem2rx`
ctlFile <- system.file("mods/cpt/runODE032.ctl", package="nonmem2rx")
# You can use a control stream or other file. With the development
# version of `babelmixr2`, you can simply point to the listing file

mod <- nonmem2rx(ctlFile, lst=".res", save=FALSE)
#> ℹ getting information from  '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl'
#> ℹ reading in xml file
#> ℹ done
#> ℹ reading in ext file
#> ℹ done
#> ℹ reading in phi file
#> ℹ done
#> ℹ reading in lst file
#> ℹ abbreviated list parsing
#> ℹ done
#> ℹ reading in grd file
#> ℹ done
#> ℹ splitting control stream by records
#> ℹ done
#> ℹ Processing record $INPUT
#> ℹ Processing record $MODEL
#> ℹ Processing record $gTHETA
#> ℹ Processing record $OMEGA
#> ℹ Processing record $SIGMA
#> ℹ Processing record $PROBLEM
#> ℹ Processing record $DATA
#> ℹ Processing record $SUBROUTINES
#> ℹ Processing record $PK
#> ℹ Processing record $DES
#> ℹ Processing record $ERROR
#> ℹ Processing record $ESTIMATION
#> ℹ Ignore record $ESTIMATION
#> ℹ Processing record $COVARIANCE
#> ℹ Ignore record $COVARIANCE
#> ℹ Processing record $TABLE
#> ℹ change initial estimate of `theta1` to `1.37034036528946`
#> ℹ change initial estimate of `theta2` to `4.19814911033061`
#> ℹ change initial estimate of `theta3` to `1.38003493562413`
#> ℹ change initial estimate of `theta4` to `3.87657341967489`
#> ℹ change initial estimate of `theta5` to `0.196446108190896`
#> ℹ change initial estimate of `eta1` to `0.101251418415006`
#> ℹ change initial estimate of `eta2` to `0.0993872449483344`
#> ℹ change initial estimate of `eta3` to `0.101302674763154`
#> ℹ change initial estimate of `eta4` to `0.0730497519364148`
#> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv
#> ℹ ignoring lines that begin with a letter (IGNORE=@)'
#> ℹ applying names specified by $INPUT
#> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),]
#> ℹ renaming 'ytype' to 'nmytype'
#> ℹ done
#> using C compiler: ‘gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0’
#> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv
#> ℹ done
#> ℹ changing most variables to lower case
#> ℹ done
#> ℹ replace theta names
#> ℹ done
#> ℹ replace eta names
#> ℹ done (no labels)
#> ℹ renaming compartments
#> ℹ done
#> ℹ solving ipred problem
#> ℹ done
#> ℹ solving pred problem
#> ℹ done

Step 2: convert the rxode2 model to nlmixr2

In this step, you convert the model to nlmixr2 by as.nlmixr2(mod); You may need to do a little manual work to get the residual specification to match between nlmixr2 and NONMEM.

Once the residual specification is compatible with a nlmixr2 object, you can convert the model, mod, to a nlmixr2 fit object:

fit <- as.nlmixr2(mod)
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of full model...
#> ✔ done
#> → finding duplicate expressions in EBE model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00
#> → optimizing duplicate expressions in EBE model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00
#> → compiling EBE model...
#> using C compiler: ‘gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0’
#> ✔ done
#> rxode2 4.0.3 using 2 threads (see ?getRxThreads)
#>   no cache: create with `rxCreateCache()`
#> → Calculating residuals/tables
#> ✔ done
#> → compress origData in nlmixr2 object, save 203816
#> → compress parHistData in nlmixr2 object, save 2184

fit

$$\begin{align*} cmt({CENTRAL}) \\ cmt({PERI}) \\ {cl} & = \exp\left({theta1}+{eta1}\right) \\ {v} & = \exp\left({theta2}+{eta2}\right) \\ {q} & = \exp\left({theta3}+{eta3}\right) \\ {v2} & = \exp\left({theta4}+{eta4}\right) \\ {v1} & = {v} \\ {scale1} & = {v} \\ {k21} & = \frac{{q}}{{v2}} \\ {k12} & = \frac{{q}}{{v}} \\ \frac{d \: CENTRAL}{dt} & = {k21} {\times} {PERI}-{k12} {\times} {CENTRAL}-\frac{{cl} {\times} {CENTRAL}}{{v1}} \\ \frac{d \: PERI}{dt} & = -{k21} {\times} {PERI}+{k12} {\times} {CENTRAL} \\ {f} & = \frac{{CENTRAL}}{{scale1}} \\ {ipred} & = {f} \\ {rescv} & = {RSV} \\ {ipred} & \sim prop({RSV}) \end{align*}$$

Step 3: Create and plot an augmented prediction

ap <- augPred(fit)
#> using C compiler: ‘gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0’

head(ap)
#>     values        ind id   time Endpoint
#> 1 1239.488 Individual  1 0.0000  CENTRAL
#> 2 1215.358 Individual  1 0.2500  CENTRAL
#> 3 1191.924 Individual  1 0.5000  CENTRAL
#> 4 1169.164 Individual  1 0.7500  CENTRAL
#> 5 1147.057 Individual  1 1.0000  CENTRAL
#> 6 1109.689 Individual  1 1.4398  CENTRAL

plot(ap)